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computing with CUDA/SYCL, MPI, Krylov solvers Preferred knowledge: Distributed preconditioners (BDDC, GDSW), CI/CD, collaborative software development. The group is situated in the new TUM Campus, in
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pipelines for reproducible, efficient and scalable training and inference on parallel, distributed and GPU-accelerated computing systems Benchmark the developed approaches against established methods
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acquisition, organization, compression, analysis, and visualization of georeferenced or geometric data on large scales. We put emphasis on methods of distributed computing, machine learning, image and
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<span lang="en">TUD Dresden University of Technology</span> | Dresden, Sachsen | Germany | about 22 hours ago
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- compiler frameworks (LLVM, MLIR)
- embedded software development
- computer and accelerator architectures
- parallel and distributed computing
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connections in the brain. It is a generally programmable computing substrate, but greatly differs from classical architectures like CPUs or GPUs, allowing for more distributed processing and simulation
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, compression, analysis, and visualization of georeferenced or geometric data on large scales. We put emphasis on methods of distributed computing, machine learning, image and text analysis, randomized data
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mathematics and who works at the intersection of numerical methods, scientific computing, and data-driven modeling. The position is devoted to the development, analysis, and implementation of flexible